Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity

An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs

BACKGROUND

Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models.

RESULTS

Here, we generate single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We develop an unsupervised machine learning approach ("automatic consensus nonnegative matrix factorization" (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirm a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly, however, this weak-mesenchymal-like program is maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 h, suggesting an uncharacterized therapy-escape mechanism.

CONCLUSIONS

Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.

Tumor initiation and early tumorigenesis: molecular mechanisms and interventional targets

Tumorigenesis is a multistep process, with oncogenic mutations in a normal cell conferring clonal advantage as the initial event. However, despite pervasive somatic mutations and clonal expansion in normal tissues, their transformation into cancer remains a rare event, indicating the presence of additional driver events for progression to an irreversible, highly heterogeneous, and invasive lesion. Recently, researchers are emphasizing the mechanisms of environmental tumor risk factors and epigenetic alterations that are profoundly influencing early clonal expansion and malignant evolution, independently of inducing mutations. Additionally, clonal evolution in tumorigenesis reflects a multifaceted interplay between cell-intrinsic identities and various cell-extrinsic factors that exert selective pressures to either restrain uncontrolled proliferation or allow specific clones to progress into tumors. However, the mechanisms by which driver events induce both intrinsic cellular competency and remodel environmental stress to facilitate malignant transformation are not fully understood. In this review, we summarize the genetic, epigenetic, and external driver events, and their effects on the co-evolution of the transformed cells and their ecosystem during tumor initiation and early malignant evolution. A deeper understanding of the earliest molecular events holds promise for translational applications, predicting individuals at high-risk of tumor and developing strategies to intercept malignant transformation.

scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network

Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.

Know your molecule: pharmacological characterization of drug candidates to enhance efficacy and reduce late-stage attrition

Despite advances in chemical, computational and biological sciences, the rate of attrition of drug candidates in clinical development is still high. A key point in the small-molecule discovery process that could provide opportunities to help address this challenge is the pharmacological characterization of hit and lead compounds, culminating in the selection of a drug candidate. Deeper characterization is increasingly important, because the 'quality' of drug efficacy, at least for G protein-coupled receptors (GPCRs), is now understood to be much more than activation of commonly evaluated pathways such as cAMP signalling, with many more 'efficacies' of ligands that could be harnessed therapeutically. Such characterization is being enabled by novel assays to characterize the complex behaviour of GPCRs, such as biased signalling and allosteric modulation, as well as advances in structural biology, such as cryo-electron microscopy. This article discusses key factors in the assessments of the pharmacology of hit and lead compounds in the context of GPCRs as a target class, highlighting opportunities to identify drug candidates with the potential to address limitations of current therapies and to improve the probability of them succeeding in clinical development.

Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations

Intratumor heterogeneity reduces treatment efficacy and complicates our understanding of tumor progression. There is a pressing need to understand the functions of heterogeneous tumor cell subpopulations within a tumor, yet biological systems to study these processes in vitro are limited. With the advent of single-cell RNA sequencing (scRNA-seq), it has become clear that some cancer cell line models include distinct subpopulations. Heterogeneous cell lines offer a unique opportunity to study the dynamics and evolution of genetically similar cancer cell subpopulations in controlled experimental settings. Here, we present clusterCleaver, a computational package that uses metrics of statistical distance to identify candidate surface markers maximally unique to transcriptomic subpopulations in scRNA-seq which may be used for FACS isolation. clusterCleaver was experimentally validated using the MDA-MB-231 and MDA-MB-436 breast cancer cell lines. ESAM and BST2/tetherin were experimentally confirmed as surface markers which identify and separate major transcriptomic subpopulations within MDA-MB-231 and MDA-MB-436 cells, respectively. clusterCleaver is a computationally efficient and experimentally validated workflow for identification and enrichment of distinct subpopulations within cell lines which paves the way for studies on the coexistence of cancer cell subpopulations in well-defined in vitro systems.

ISMI-VAE: A deep learning model for classifying disease cells using gene expression and SNV data

Various studies have linked several diseases, including cancer and COVID-19, to single nucleotide variations (SNV). Although single-cell RNA sequencing (scRNA-seq) technology can provide SNV and gene expression data, few studies have integrated and analyzed these multimodal data. To address this issue, we introduce Interpretable Single-cell Multimodal Data Integration Based on Variational Autoencoder (ISMI-VAE). ISMI-VAE leverages latent variable models that utilize the characteristics of SNV and gene expression data to overcome high noise levels and uses deep learning techniques to integrate multimodal information, map them to a low-dimensional space, and classify disease cells. Moreover, ISMI-VAE introduces an attention mechanism to reflect feature importance and analyze genetic features that could potentially cause disease. Experimental results on three cancer data sets and one COVID-19 data set demonstrate that ISMI-VAE surpasses the baseline method in terms of both effectiveness and interpretability and can effectively identify disease-causing gene features.

The tumor-intrinsic role of the m6A reader YTHDF2 in regulating immune evasion

Tumors evade attacks from the immune system through various mechanisms. Here, we identify a component of tumor immune evasion mediated by YTH domain-containing family protein 2 (YTHDF2), a reader protein that usually destabilizes m6A-modified mRNA. Loss of tumoral YTHDF2 inhibits tumor growth and prolongs survival in immunocompetent tumor models. Mechanistically, tumoral YTHDF2 deficiency promotes the recruitment of macrophages via CX3CL1 and enhances mitochondrial respiration of CD8+ T cells by impairing tumor glycolysis metabolism. Tumoral YTHDF2 deficiency promotes inflammatory macrophage polarization and antigen presentation in the presence of IFN-γ. In addition, IFN-γ induces autophagic degradation of tumoral YTHDF2, thereby sensitizing tumor cells to CD8+ T cell-mediated cytotoxicity. Last, we identified a small molecule compound that preferentially induces YTHDF2 degradation, which shows a potent antitumor effect alone but a better effect when combined with anti-PD-L1 or anti-PD-1 antibodies. Collectively, YTHDF2 appears to be a tumor-intrinsic regulator that orchestrates immune evasion, representing a promising target for enhancing cancer immunotherapy.

Dissecting tumor transcriptional heterogeneity from single-cell RNA-seq data by generalized binary covariance decomposition

Profiling tumors with single-cell RNA sequencing (scRNA-seq) has the potential to identify recurrent patterns of transcription variation related to cancer progression, and produce new therapeutically relevant insights. However, the presence of strong inter-tumor heterogeneity often obscures more subtle patterns that are shared across tumors, some of which may characterize clinically relevant disease subtypes. Here we introduce a new statistical method, generalized binary covariance decomposition (GBCD), to address this problem. We show that GBCD can help decompose transcriptional heterogeneity into interpretable components - including patient-specific, dataset-specific and shared components relevant to disease subtypes - and that, in the presence of strong inter-tumor heterogeneity, it can produce more interpretable results than existing methods. Applied to data from three studies on pancreatic cancer adenocarcinoma (PDAC), GBCD produces a refined characterization of existing tumor subtypes (e.g., classical vs. basal), and identifies a new gene expression program (GEP) that is prognostic of poor survival independent of established prognostic factors such as tumor stage and subtype. The new GEP is enriched for genes involved in a variety of stress responses, and suggests a potentially important role for the integrated stress response in PDAC development and prognosis.

Nanostructured Biomaterials in 3D Tumor Tissue Engineering Scaffolds: Regenerative Medicine and Immunotherapies

The evaluation of nanostructured biomaterials and medicines is associated with 2D cultures that provide insight into biological mechanisms at the molecular level, while critical aspects of the tumor microenvironment (TME) are provided by the study of animal xenograft models. More realistic models that can histologically reproduce human tumors are provided by tissue engineering methods of co-culturing cells of varied phenotypes to provide 3D tumor spheroids that recapitulate the dynamic TME in 3D matrices. The novel approaches of creating 3D tumor models are combined with tumor tissue engineering (TTE) scaffolds including hydrogels, bioprinted materials, decellularized tissues, fibrous and nanostructured matrices. This review focuses on the use of nanostructured materials in cancer therapy and regeneration, and the development of realistic models for studying TME molecular and immune characteristics. Tissue regeneration is an important aspect of TTE scaffolds used for restoring the normal function of the tissues, while providing cancer treatment. Thus, this article reports recent advancements in the development of 3D TTE models for antitumor drug screening, studying tumor metastasis, and tissue regeneration. Also, this review identifies the significant opportunities of using 3D TTE scaffolds in the evaluation of the immunological mechanisms and processes involved in the application of immunotherapies.

Integrative spatial analysis reveals a multi-layered organization of glioblastoma

Glioma contains malignant cells in diverse states. Here, we combine spatial transcriptomics, spatial proteomics, and computational approaches to define glioma cellular states and uncover their organization. We find three prominent modes of organization. First, gliomas are composed of small local environments, each typically enriched with one major cellular state. Second, specific pairs of states preferentially reside in proximity across multiple scales. This pairing of states is consistent across tumors. Third, these pairwise interactions collectively define a global architecture composed of five layers. Hypoxia appears to drive the layers, as it is associated with a long-range organization that includes all cancer cell states. Accordingly, tumor regions distant from any hypoxic/necrotic foci and tumors that lack hypoxia such as low-grade IDH-mutant glioma are less organized. In summary, we provide a conceptual framework for the organization of cellular states in glioma, highlighting hypoxia as a long-range tissue organizer.

Establishment and characterization of two novel patient-derived cell lines from giant cell tumor of bone: NCC-GCTB8-C1 and NCC-GCTB9-C1

Giant cell tumor of bone (GCTB) is a rare osteolytic bone tumor consisting of mononuclear stromal cells, macrophages, and osteoclast-like giant cells. Although GCTB predominantly exhibits benign behavior, the tumor carries a significant risk of high local recurrence. Furthermore, GCTB can occasionally undergo malignant transformation and distal metastasis, making it potentially fatal. The standard treatment is complete surgical resection; nonetheless, an optimal treatment strategy for advanced GCTB remains unestablished, necessitating expanded preclinical research to identify appropriate therapeutic options. However, only one GCTB cell line is publicly available from a cell bank for research use worldwide. The present study reports the establishment of two novel cell lines, NCC-GCTB8-C1 and NCC-GCTB9-C1, derived from the primary tumor tissues of two patients with GCTB. Both cell lines maintained the hallmark mutation in the H3-3A gene, which is associated with tumor formation and development in GCTB. Characterization of these cell lines revealed their steady growth, spheroid-formation capability, and invasive traits. Potential therapeutic agents were identified via extensive drug screening of the two cell lines and seven previously established GCTB cell lines. Among the 214 antitumor agents tested, romidepsin, a histone deacetylase inhibitor, and mitoxantrone, a topoisomerase inhibitor, were identified as potential therapeutic agents against GCTB. Conclusively, the establishment of NCC-GCTB8-C1 and NCC-GCTB9-C1 provides novel and crucial resources that are expected to advance GCTB research and potentially revolutionize treatment strategies.

An integrated single-cell RNA-seq map of human neuroblastoma tumors and preclinical models uncovers divergent mesenchymal-like gene expression programs

Neuroblastoma is a common pediatric cancer, where preclinical studies suggest that a mesenchymal-like gene expression program contributes to chemotherapy resistance. However, clinical outcomes remain poor, implying we need a better understanding of the relationship between patient tumor heterogeneity and preclinical models. Here, we generated single-cell RNA-seq maps of neuroblastoma cell lines, patient-derived xenograft models (PDX), and a genetically engineered mouse model (GEMM). We developed an unsupervised machine learning approach ('automatic consensus nonnegative matrix factorization' (acNMF)) to compare the gene expression programs found in preclinical models to a large cohort of patient tumors. We confirmed a weakly expressed, mesenchymal-like program in otherwise adrenergic cancer cells in some pre-treated high-risk patient tumors, but this appears distinct from the presumptive drug-resistance mesenchymal programs evident in cell lines. Surprisingly however, this weak-mesenchymal-like program was maintained in PDX and could be chemotherapy-induced in our GEMM after only 24 hours, suggesting an uncharacterized therapy-escape mechanism. Collectively, our findings improve the understanding of how neuroblastoma patient tumor heterogeneity is reflected in preclinical models, provides a comprehensive integrated resource, and a generalizable set of computational methodologies for the joint analysis of clinical and pre-clinical single-cell RNA-seq datasets.

PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors

Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.

Predicting drug response through tumor deconvolution by cancer cell lines

Large-scale cancer drug sensitivity data have become available for a collection of cancer cell lines, but only limited drug response data from patients are available. Bridging the gap in pharmacogenomics knowledge between in vitro and in vivo datasets remains challenging. In this study, we trained a deep learning model, Scaden-CA, for deconvoluting tumor data into proportions of cancer-type-specific cell lines. Then, we developed a drug response prediction method using the deconvoluted proportions and the drug sensitivity data from cell lines. The Scaden-CA model showed excellent performance in terms of concordance correlation coefficients (>0.9 for model testing) and the correctly deconvoluted rate (>70% across most cancers) for model validation using Cancer Cell Line Encyclopedia (CCLE) bulk RNA data. We applied the model to tumors in The Cancer Genome Atlas (TCGA) dataset and examined associations between predicted cell viability and mutation status or gene expression levels to understand underlying mechanisms of potential value for drug repurposing.

Single-cell characterization of infiltrating T cells identifies novel targets for gallbladder cancer immunotherapy

Gallbladder cancer (GBC) is among the most common malignancies of biliary tract system due to its limited treatments. The immunotherapeutic targets for T cells are appealing, however, heterogeneity of T cells hinds its further development. We systematically construct T cell atlas by single-cell RNA sequencing; and utilized the identified gene signatures of high_CNV_T cells to predict molecular subtyping towards personalized therapeutic treatments for GBC. We identified 12 T cell subtypes, where exhausted CD8+ T cells, activated/exhausted CD8+ T cells, and regulatory T cells were predominant in tumors. There appeared to be an inverse relationship between Th17 and Treg populations with Th17 levels significantly reduced, whereas Tregs were concomitantly increased. Furthermore, we first established subtyping criterion to identify three subtypes of GBC based on their pro-tumorigenic microenvironments, e.g., the type 1 group shows more M2 macrophages infiltration, while the type 2 group is infiltrated by highly exhausted CD8+ T cells, B cells and Tregs with suppressive activities. Our study provides valuable insights into T cell heterogeneity and suggests that molecular subtyping based on T cells might provide a potential immunotherapeutic strategy to improve GBC treatment.

Up-regulation of RAN by MYBL2 maintains osteosarcoma cancer stem-like cells population during heterogeneous tumor generation

Intratumor heterogeneity is one of the major features of cancers, leading to aggressive disease and treatment failure. Cancer stem-like cells (CSCs) are believed to give rise to the heterogeneous cell types within tumors. Hence, understanding the regulatory mechanism underlying the recurrence process of heterogeneous tumor by CSCs could facilitate the development of CSC-targeted therapies. Here, utilizing single-cell transcriptomics, we present the molecular profile of osteosarcoma CSCs-derived heterogeneous tumors consisting of CSC clusters, osteoprogenitor and differentiated cell types, such as pre-osteoblasts, osteoblasts and chondroblasts. Furthermore, by constructing the comprehensive map of modulated genes during CSCs self-renewal and differentiation, we identify RAN exhibiting specific peak expression in osteosarcoma CSCs clusters which is transcriptionally up-regulated by MYBL2. Functionality, MYBL2-RAN pathway promotes the CSCs self-renewal by enhancing the nuclear accumulation of MYC protein, which in turn boosts the overexpression of RAN as a positive feedback. Importantly, blockage of MYBL2-RAN pathway sensitizes CSCs to cisplatin treatment and synergistically enhanced the cisplatin-induced cytotoxicity. Both MYBL2 and RAN are highly expressed in clinical osteosarcoma tissues which indicate poor prognosis. Collectively, our study provides advanced insights into the regeneration process of heterogeneous tumor originating from CSCs and highlights the MYBL2-RAN pathway as a promising target for CSC-based therapy in osteosarcoma.

Macrophage heterogeneity in bone metastasis

Previous studies illustrated that macrophage, a type of innate immune cell, plays critical roles in tumour progression and metastasis. Bone is the most frequent site of metastasis for several cancer types including breast, prostate, and lung. In bone metastasis, osteoclast, a macrophage subset specialized in bone resorption, was heavily investigated in the past. Recent studies illustrated that other macrophage subsets, e.g. monocyte-derived macrophages, and bone resident macrophages, promoted bone metastasis independent of osteoclast function. These novel mechanisms further improved our understanding of macrophage heterogeneity in the context of bone metastasis and illustrated new opportunities for future studies.

From complexity to clarity: unravelling tumor heterogeneity through the lens of tumor microenvironment for innovative cancer therapy

Despite the tremendous clinical successes recorded in the landscape of cancer therapy, tumor heterogeneity remains a formidable challenge to successful cancer treatment. In recent years, the emergence of high-throughput technologies has advanced our understanding of the variables influencing tumor heterogeneity beyond intrinsic tumor characteristics. Emerging knowledge shows that drivers of tumor heterogeneity are not only intrinsic to cancer cells but can also emanate from their microenvironment, which significantly favors tumor progression and impairs therapeutic response. Although much has been explored to understand the fundamentals of the influence of innate tumor factors on cancer diversity, the roles of the tumor microenvironment (TME) are often undervalued. It is therefore imperative that a clear understanding of the interactions between the TME and other tumor intrinsic factors underlying the plastic molecular behaviors of cancers be identified to develop patient-specific treatment strategies. This review highlights the roles of the TME as an emerging factor in tumor heterogeneity. More particularly, we discuss the role of the TME in the context of tumor heterogeneity and explore the cutting-edge diagnostic and therapeutic approaches that could be used to resolve this recurring clinical conundrum. We conclude by speculating on exciting research questions that can advance our understanding of tumor heterogeneity with the goal of developing customized therapeutic solutions.

Improving drug response prediction via integrating gene relationships with deep learning

Predicting the drug response of cancer cell lines is crucial for advancing personalized cancer treatment, yet remains challenging due to tumor heterogeneity and individual diversity. In this study, we present a deep learning-based framework named Deep neural network Integrating Prior Knowledge (DIPK) (DIPK), which adopts self-supervised techniques to integrate multiple valuable information, including gene interaction relationships, gene expression profiles and molecular topologies, to enhance prediction accuracy and robustness. We demonstrated the superior performance of DIPK compared to existing methods on both known and novel cells and drugs, underscoring the importance of gene interaction relationships in drug response prediction. In addition, DIPK extends its applicability to single-cell RNA sequencing data, showcasing its capability for single-cell-level response prediction and cell identification. Further, we assess the applicability of DIPK on clinical data. DIPK accurately predicted a higher response to paclitaxel in the pathological complete response (pCR) group compared to the residual disease group, affirming the better response of the pCR group to the chemotherapy compound. We believe that the integration of DIPK into clinical decision-making processes has the potential to enhance individualized treatment strategies for cancer patients.

Nephroblastoma-specific dysregulated gene SNHG15 with prognostic significance: scRNA-Seq with bulk RNA-Seq data and experimental validation

Wilms tumor (WT) is the most common malignancy of the genitourinary system in children. Currently, the Integration of single-cell RNA sequencing (scRNA-Seq) and Bulk RNA sequencing (RNA-Seq) analysis of heterogeneity between different cell types in pediatric WT tissues could more accurately find prognostic markers, but this is lacking. RNA-Seq and clinical data related to WT were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Small nucleolar RNA host gene 15 (SNHG15) was identified as a risk signature from the TARGET dataset by using weighted gene co-expression network analysis, differentially expressed analysis and univariate Cox analysis. After that, the functional mechanisms, immunological and molecular characterization of SNHG15 were investigated at the scRNA-seq, pan-cancer, and RNA-seq levels using Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), ESTIMATE, and CIBERSORT. Based on scRNA-seq data, we identified 20 clusters in WT and annotated 10 cell types. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing M2 macrophages as hubs for intercellular communication. In addition, in vitro cellular experiments showed that siRNAs interfering with SNHG15 significantly inhibited the proliferation and migration of G401 cells and promoted the apoptosis of G401 cells compared with the control group. The effect of siRNAs interfering with SNHG15 on EMT-related protein expression was verified by Western blotting assay. Thus, our findings will improve our current understanding of the pathogenesis of WT, and they are potentially valuable in providing novel prognosis markers for the treatment of WT.

Targeting of vulnerabilities of drug-tolerant persisters identified through functional genetics delays tumor relapse

Drug-tolerant persisters (DTPs) are a rare subpopulation of cells within a tumor that can survive therapy through nongenetic adaptive mechanisms to develop relapse and repopulate the tumor following drug withdrawal. Using a cancer cell line with an engineered suicide switch to kill proliferating cells, we perform both genetic screens and compound screens to identify the inhibition of bromodomain and extraterminal domain (BET) proteins as a selective vulnerability of DTPs. BET inhibitors are especially detrimental to DTPs that have reentered the cell cycle (DTEPs) in a broad spectrum of cancer types. Mechanistically, BET inhibition induces lethal levels of ROS through the suppression of redox-regulating genes highly expressed in DTPs, including GPX2, ALDH3A1, and MGST1. In vivo BET inhibitor treatment delays tumor relapse in both melanoma and lung cancer. Our study suggests that combining standard of care therapy with BET inhibitors to eliminate residual persister cells is a promising therapeutic strategy.

Neutrophil profiling illuminates anti-tumor antigen-presenting potency

Neutrophils, the most abundant and efficient defenders against pathogens, exert opposing functions across cancer types. However, given their short half-life, it remains challenging to explore how neutrophils adopt specific fates in cancer. Here, we generated and integrated single-cell neutrophil transcriptomes from 17 cancer types (225 samples from 143 patients). Neutrophils exhibited extraordinary complexity, with 10 distinct states including inflammation, angiogenesis, and antigen presentation. Notably, the antigen-presenting program was associated with favorable survival in most cancers and could be evoked by leucine metabolism and subsequent histone H3K27ac modification. These neutrophils could further invoke both (neo)antigen-specific and antigen-independent T cell responses. Neutrophil delivery or a leucine diet fine-tuned the immune balance to enhance anti-PD-1 therapy in various murine cancer models. In summary, these data not only indicate the neutrophil divergence across cancers but also suggest therapeutic opportunities such as antigen-presenting neutrophil delivery.

Single-cell integrative analysis reveals consensus cancer cell states and clinical relevance in breast cancer

High heterogeneity and complex interactions of malignant cells in breast cancer has been recognized as a driver of cancer progression and therapeutic failure. However, complete understanding of common cancer cell states and their underlying driver factors remain scarce and challenging. Here, we revealed seven consensus cancer cell states recurring cross patients by integrative analysis of single-cell RNA sequencing data of breast cancer. The distinct biological functions, the subtype-specific distribution, the potential cells of origin and the interrelation of consensus cancer cell states were systematically elucidated and validated in multiple independent datasets. We further uncovered the internal regulons and external cell components in tumor microenvironments, which contribute to the consensus cancer cell states. Using the state-specific signature, we also inferred the abundance of cells with each consensus cancer cell state by deconvolution of large breast cancer RNA-seq cohorts, revealing the association of immune-related state with better survival. Our study provides new insights for the cancer cell state composition and potential therapeutic strategies of breast cancer.

Functional transcriptional signatures for tumor-type-agnostic phenotype prediction

Cancer transcriptional patterns exhibit both shared and unique features across diverse cancer types, but whether these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that cancer transcriptional diversity mirrors patterns in normal tissues optimized for distinct functional tasks. Starting with normal tissue transcriptomic profiles, we use non-negative matrix factorization to derive six distinct transcriptomic phenotypes, called archetypes, which combine to describe both normal tissue patterns and variations across a broad spectrum of malignancies. We show that differential enrichment of these signatures correlates with key tumor characteristics, including overall patient survival and drug sensitivity, independent of clinically actionable DNA alterations. Additionally, we show that in HR+/HER2- breast cancers, metastatic tumors adopt transcriptomic signatures consistent with the invaded tissue. Broadly, our findings suggest that cancer often arrogates normal tissue transcriptomic characteristics as a component of both malignant progression and drug response. This quantitative framework provides a strategy for connecting the diversity of cancer phenotypes and could potentially help manage individual patients.

Cooperative pro-tumorigenic adaptation to oncogenic RAS through epithelial-to-mesenchymal plasticity

In breast cancers, aberrant activation of the RAS/MAPK pathway is strongly associated with mesenchymal features and stemness traits, suggesting an interplay between this mitogenic signaling pathway and epithelial-to-mesenchymal plasticity (EMP). By using inducible models of human mammary epithelial cells, we demonstrate herein that the oncogenic activation of RAS promotes ZEB1-dependent EMP, which is necessary for malignant transformation. Notably, EMP is triggered by the secretion of pro-inflammatory cytokines from neighboring RAS-activated senescent cells, with a prominent role for IL-6 and IL-1α. Our data contrast with the common view of cellular senescence as a tumor-suppressive mechanism and EMP as a process promoting late stages of tumor progression in response to signals from the tumor microenvironment. We highlighted here a pro-tumorigenic cooperation of RAS-activated mammary epithelial cells, which leverages on oncogene-induced senescence and EMP to trigger cellular reprogramming and malignant transformation.

A cellular and spatial atlas of TP53 -associated tissue remodeling in lung adenocarcinoma

TP53 is the most frequently mutated gene across many cancers and is associated with shorter survival in lung adenocarcinoma (LUAD). To define how TP53 mutations affect the LUAD tumor microenvironment (TME), we constructed a multi-omic cellular and spatial tumor atlas of 23 treatment-naïve human lung tumors. We found that TP53 -mutant ( TP53 mut ) malignant cells lose alveolar identity and upregulate highly proliferative and entropic gene expression programs consistently across resectable LUAD patient tumors, genetically engineered mouse models, and cell lines harboring a wide spectrum of TP53 mutations. We further identified a multicellular tumor niche composed of SPP1 + macrophages and collagen-expressing fibroblasts that coincides with hypoxic, pro-metastatic expression programs in TP53 mut tumors. Spatially correlated angiostatic and immune checkpoint interactions, including CD274 - PDCD1 and PVR - TIGIT , are also enriched in TP53 mut LUAD tumors, which may influence response to checkpoint blockade therapy. Our methodology can be further applied to investigate mutation-specific TME changes in other cancers.

Functional analysis of cell plasticity using single-cell technologies

Metazoan organisms are heterocellular systems composed of hundreds of different cell types, which arise from an isogenic genome through differentiation. Cellular 'plasticity' further enables cells to alter their fate in response to exogenous cues and is involved in a variety of processes, such as wound healing, infection, and cancer. Recent advances in cellular model systems, high-dimensional single-cell technologies, and lineage tracing have sparked a renaissance in plasticity research. Here, we discuss the definition of cell plasticity, evaluate state-of-the-art model systems and techniques to study cell-fate dynamics, and explore the application of single-cell technologies to obtain functional insights into cell plasticity in healthy and diseased tissues. The integration of advanced biomimetic model systems, single-cell technologies, and high-throughput perturbation studies is enabling a new era of research into non-genetic plasticity in metazoan systems.

Deciphering the heterogeneity of neutrophil cells within circulation and the lung cancer microenvironment pre- and post-operation

Neutrophils play a crucial role in the immune system within tumor microenvironment. At present, numerous studies have explored the changes of neutrophils' automatic killing effect and cellular communication with other immune cells under pathological conditions through single-cell sequencing. However, there remains a lack of definite conclusion about the identification criteria of neutrophil subgroups. Here, we collected tumor and para-carcinoma tissues, pre- and postoperative blood from patients with non-small cell lung cancer (NSCLC), and performed single-cell RNA (scRNA) sequencing to evaluate the distribution of neutrophil subgroups. We have developed a computational method of over expression rate (OER) to evaluate the specificity of neutrophil subgroups, in order to target gene panels with potential clinical application value. In addition, OER was used to evaluate specificity of neutrophil subsets in healthy people and patients with various diseases to further validate the feasibility of this evaluation system. As a result, we found the specificity of Neu_ c1_ IL1B and Neu_ c2_ cxcr4 (low) in postoperative blood has increased, while that of IL-7R + neutrophils has decreased, indicating that these groups of cells possibly differentiated or migrated to other subgroups in the state of lung cancer. In addition, seven gene panels (Neu_c3_CST7, RSAD2_Neu, S100A2/Pabpc1_Neu, ISG15/Ifit3_Neu, CD74_Neu, PTGS2/Actg1_Neu, SPP1_Neu) were high specific in all the four NSCLC-associated samples, meaning that changes in the percentage of these cell populations would have a high degree of confidence in assessing changes of disease status. In conclusion, combined consideration of the distribution characteristics of neutrophil subgroups could help evaluate the diagnosis and prognosis of NSCLC.

CENPN suppresses autophagy and increases paclitaxel resistance in nasopharyngeal carcinoma cells by inhibiting the CREB-VAMP8 signaling axis

Chemotherapeutic resistance is one of the most common reasons for poor prognosis of patients with nasopharyngeal carcinoma (NPC). We found that CENPN can promote the growth, proliferation and apoptosis resistance of NPC cells, but its relationship with chemotherapeutic resistance in NPC is unclear. Here we verified that the CENPN expression level in NPC patients was positively correlated with the degree of paclitaxel (PTX) resistance and a poor prognosis through analysis of clinical cases. VAMP8 expression was significantly increased after knockdown of CENPN by transcriptome sequencing. We found in cell experiments that CENPN inhibited macroautophagy/autophagy and VAMP8 expression and significantly increased PTX resistance. Overexpression of CENPN reduced the inhibitory effects of PTX on survival, cell proliferation, cell cycle progression and apoptosis resistance in NPC cells by inhibiting autophagy. In turn, knockdown of CENPN can affect the phenotype of NPC cells by increasing autophagy to achieve PTX sensitization. Sequential knockdown of CENPN and VAMP8 reversed the PTX-sensitizing effect of CENPN knockdown alone. Experiments in nude mice confirmed that knockdown of CENPN can increase VAMP8 expression, enhance autophagy and increase the sensitivity of NPC cells to PTX. Mechanistic studies showed that CENPN inhibited the translocation of p-CREB into the nucleus of NPC cells, resulting in the decreased binding of p-CREB to the VAMP8 promoter, thereby inhibiting the transcription of VAMP8. These results demonstrate that CENPN may be a marker for predicting chemotherapeutic efficacy and a potential target for inducing chemosensitization to agents such as PTX.Abbreviations: 3-MA: 3-methyladenine; ATG5: autophagy related 5; CENPN: centromere protein N; CQ: chloroquine; CREB: cAMP responsive element binding protein; ChIP: chromatin immunoprecipitation assay; IC50: half-maximal inhibitory concentration; LAMP2A: lysosomal associated membrane protein 2A; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; NPC: nasopharyngeal carcinoma; NPG: nasopharyngitis; oeCENPN: overexpressed CENPN; PTX: paclitaxel; RAPA: rapamycin; RNA-seq: transcriptome sequencing; shCENPN: small hairpin RNA expression vector targeting the human CENPN gene; shCENPN-shVAMP8: sequential knockdown targeting the human CENPN gene and VAMP8 gene; shVAMP8: small hairpin RNA expression vector targeting the human VAMP8 gene; TEM: transmission electron microscopy; TIR: tumor inhibitory rate; VAMP8: vesicle associated membrane protein 8.

Pan-Cancer Single-Nucleus Total RNA Sequencing Using snHH-Seq

Tumor heterogeneity and its drivers impair tumor progression and cancer therapy. Single-cell RNA sequencing is used to investigate the heterogeneity of tumor ecosystems. However, most methods of scRNA-seq amplify the termini of polyadenylated transcripts, making it challenging to perform total RNA analysis and somatic mutation analysis.Therefore, a high-throughput and high-sensitivity method called snHH-seq is developed, which combines random primers and a preindex strategy in the droplet microfluidic platform. This innovative method allows for the detection of total RNA in single nuclei from clinically frozen samples. A robust pipeline to facilitate the analysis of full-length RNA-seq data is also established. snHH-seq is applied to more than 730 000 single nuclei from 32 patients with various tumor types. The pan-cancer study enables it to comprehensively profile data on the tumor transcriptome, including expression levels, mutations, splicing patterns, clone dynamics, etc. New malignant cell subclusters and exploring their specific function across cancers are identified. Furthermore, the malignant status of epithelial cells is investigated among different cancer types with respect to mutation and splicing patterns. The ability to detect full-length RNA at the single-nucleus level provides a powerful tool for studying complex biological systems and has broad implications for understanding tumor pathology.

Cell cycle gene alterations associate with a redistribution of mutation risk across chromosomal domains in human cancers

Mutations in human cells exhibit increased burden in heterochromatic, late DNA replication time (RT) chromosomal domains, with variation in mutation rates between tissues mirroring variation in heterochromatin and RT. We observed that regional mutation risk further varies between individual tumors in a manner independent of cell type, identifying three signatures of domain-scale mutagenesis in >4,000 tumor genomes. The major signature reflects remodeling of heterochromatin and of the RT program domains seen across tumors, tissues and cultured cells, and is robustly linked with higher expression of cell proliferation genes. Regional mutagenesis is associated with loss of activity of the tumor-suppressor genes RB1 and TP53, consistent with their roles in cell cycle control, with distinct mutational patterns generated by the two genes. Loss of regional heterogeneity in mutagenesis is associated with deficiencies in various DNA repair pathways. These mutation risk redistribution processes modify the mutation supply towards important genes, diverting the course of somatic evolution.

Deciphering the tumor-suppressive role of PSMB9 in melanoma through multi-omics and single-cell transcriptome analyses

Skin cutaneous melanoma (SKCM) poses a significant challenge in skin cancers. Recent immunotherapy breakthroughs have revolutionized melanoma treamtment, yet tumor heterogeneity persists as an obstacle. Epigenetic modifications orchestrated by DNA methylation contributed to tumorigenesis, thus potentially unveiling melanoma prognosis. Here, we identified an interferon-gamma (IFN-g) sensitive subtype, which possesses favorable outcomes, robust infiltration CD8+T cells, and IFN-g score in bulk RNA-seq profile. Subsequently, we established an IFN-g sensitivity signature based on machine learning. We validated that PSMB9 is strongly correlated with immunotherapy response in both methylation and expression cohorts in this 10-probe signature. We assumed that PSMB9 acts as a putative melanoma suppressor, for its activation of CD8+T cell; capacity to modulate IFN-γ secretion; and dynamics altering IFN-g receptors in bulk tissue. We performed single-cell RNA-seq on immunotherapy patients' tissue to uncover the nuanced role of PSMB9 in activating CD8T + cells, enhancing IFN-g, and influencing malignant cells receptors and transcriptional factors. Overexpress PSMB9 in two SKCM cell lines to mimic the hypomethylated state to approve our conjecture. Strong cell proliferation and migration inhibition were detected on both cells, indicating that PSMB9 is present in tumor cells and that high expression is detrimental to tumor growth and migration. Overall, comprehensive integrated analysis shows that PSMB9 emerges as a vital prognostic marker, acting predictive potential regarding immunotherapy in melanoma. This evidence not only reveals the multifaceted impact of PSMB9 on both malignant and immune cells but also serves as a prospective target for undergoing immunotherapeutic strategies in the future.

CHOIR improves significance-based detection of cell types and states from single-cell data

Clustering is a critical step in the analysis of single-cell data, as it enables the discovery and characterization of putative cell types and states. However, most popular clustering tools do not subject clustering results to statistical inference testing, leading to risks of overclustering or underclustering data and often resulting in ineffective identification of cell types with widely differing prevalence. To address these challenges, we present CHOIR (clustering hierarchy optimization by iterative random forests), which applies a framework of random forest classifiers and permutation tests across a hierarchical clustering tree to statistically determine which clusters represent distinct populations. We demonstrate the enhanced performance of CHOIR through extensive benchmarking against 14 existing clustering methods across 100 simulated and 4 real single-cell RNA-seq, ATAC-seq, spatial transcriptomic, and multi-omic datasets. CHOIR can be applied to any single-cell data type and provides a flexible, scalable, and robust solution to the important challenge of identifying biologically relevant cell groupings within heterogeneous single-cell data.

Claudin-4 modulates autophagy via SLC1A5/LAT1 as a tolerance mechanism for genomic instability in ovarian cancer

Genome instability is key for tumor heterogeneity and derives from defects in cell division and DNA damage repair. Tumors show tolerance for this characteristic, but its accumulation is regulated somehow to avoid catastrophic chromosomal alterations and cell death. Claudin-4 is upregulated and closely associated with genome instability and worse patient outcome in ovarian cancer. This protein is commonly described as a junctional protein participating in processes such as cell proliferation and DNA repair. However, its biological association with genomic instability is still poorly-understood. Here, we used CRISPRi and a claudin mimic peptide (CMP) to modulate the cladudin-4 expression and its function, respectively in in-vitro (high-grade serous carcinoma cells) and in-vivo (patient-derived xenograft in a humanized-mice model) systems. We found that claudin-4 promotes a protective cellular-mechanism that links cell-cell junctions to genome integrity. Disruption of this axis leads to irregular cellular connections and cell cycle that results in chromosomal alterations, a phenomenon associated with a novel functional link between claudin-4 and SLC1A5/LAT1 in regulating autophagy. Consequently, claudin-4's disruption increased autophagy and associated with engulfment of cytoplasm-localized DNA. Furthermore, the claudin-4/SLC1A5/LAT1 biological axis correlates with decrease ovarian cancer patient survival and targeting claudin-4 in-vivo with CMP resulted in increased niraparib (PARPi) efficacy, correlating with increased tumoral infiltration of T CD8+ lymphocytes. Our results show that the upregulation of claudin-4 enables a mechanism that promotes tolerance to genomic instability and immune evasion in ovarian cancer; thus, suggesting the potential of claudin-4 as a translational target for enhancing ovarian cancer treatment.

Cancer Evolution: A Multifaceted Affair

Cancer cells adapt and survive through the acquisition and selection of molecular modifications. This process defines cancer evolution. Building on a theoretical framework based on heritable genetic changes has provided insights into the mechanisms supporting cancer evolution. However, cancer hallmarks also emerge via heritable nongenetic mechanisms, including epigenetic and chromatin topological changes, and interactions between tumor cells and the tumor microenvironment. Recent findings on tumor evolutionary mechanisms draw a multifaceted picture where heterogeneous forces interact and influence each other while shaping tumor progression. A comprehensive characterization of the cancer evolutionary toolkit is required to improve personalized medicine and biomarker discovery.

SIGNIFICANCE

Tumor evolution is fueled by multiple enabling mechanisms. Importantly, genetic instability, epigenetic reprogramming, and interactions with the tumor microenvironment are neither alternative nor independent evolutionary mechanisms. As demonstrated by findings highlighted in this perspective, experimental and theoretical approaches must account for multiple evolutionary mechanisms and their interactions to ultimately understand, predict, and steer tumor evolution.

Impact of platelet-derived mitochondria transfer in the metabolic profiling and progression of metastatic MDA-MB-231 human triple-negative breast cancer cells

Introduction: An active role of platelets in the progression of triple-negative breast cancer (TNBC) cells has been described. Even the role of platelet-derived extracellular vesicles on the migration of MDA-MB-231 cells has been reported. Interestingly, upon activation, platelets release functional mitochondria into the extracellular environment. However, the impact of these platelet-derived mitochondria on the metabolic properties of MDA-MB-231 cells remains unclear. Methods: MDA-MB-231 and MDA-MB-231-Rho-0 cells were co-cultured with platelets, which were isolated from donor blood. Mitochondrial transfer was assessed through confocal microscopy and flow cytometry, while metabolic analyses were conducted using a Seahorse XF HS Mini Analyzer. The mito-chondrial DNA (mtDNA) copy number was determined via quantitative PCR (qPCR) following platelet co-culture. Finally, cell proliferation and colony formation assay were performed using crystal violet staining. Results and Discussion: We have shown that platelet-derived mitochondria are internalized by MDA-MB-231 cells in co-culture with platelets, increasing ATP production, oxygen (O2) consumption rate (OCR), cell proliferation, and metabolic adaptability. Additionally, we observed that MDA-MB-231 cells depleted from mtDNA restore cell proliferation in uridine/pyruvate-free cell culture medium and mitochondrial O2 consumption after co-culture with platelets, indicating a reconstitution of mtDNA facilitated by platelet-derived mitochondria. In conclusion, our study provides new insights into the role of platelet-derived mitochondria in the metabolic adaptability and progression of metastatic MDA-MB-231 TNBC cells.

Single-cell multi-omic analysis of the vestibular schwannoma ecosystem uncovers a nerve injury-like state

Vestibular schwannomas (VS) are benign tumors that lead to significant neurologic and otologic morbidity. How VS heterogeneity and the tumor microenvironment (TME) contribute to VS pathogenesis remains poorly understood. In this study, we perform scRNA-seq on 15 VS, with paired scATAC-seq (n = 6) and exome sequencing (n = 12). We identify diverse Schwann cell (SC), stromal, and immune populations in the VS TME and find that repair-like and MHC-II antigen-presenting SCs are associated with myeloid cell infiltrate, implicating a nerve injury-like process. Deconvolution analysis of RNA-expression data from 175 tumors reveals Injury-like tumors are associated with larger tumor size, and scATAC-seq identifies transcription factors associated with nerve repair SCs from Injury-like tumors. Ligand-receptor analysis and in vitro experiments suggest that Injury-like VS-SCs recruit myeloid cells via CSF1 signaling. Our study indicates that Injury-like SCs may cause tumor growth via myeloid cell recruitment and identifies molecular pathways that may be therapeutically targeted.

Integrative analysis of single-cell and bulk RNA-sequencing data revealed T cell marker genes based molecular sub-types and a prognostic signature in lung adenocarcinoma

Immunotherapy has emerged as a promising modality for addressing advanced or conventionally drug-resistant malignancies. When it comes to lung adenocarcinoma (LUAD), T cells have demonstrated significant influence on both antitumor activity and the tumor microenvironment. However, their specific contributions remain largely unexplored. This investigation aimed to delineate molecular subtypes and prognostic indicators founded on T cell marker genes, thereby shedding light on the significance of T cells in LUAD prognosis and precision treatment. The cellular phenotypes were identified by scrutinizing the single-cell data obtained from the GEO repository. Subsequently, T cell marker genes derived from single-cell sequencing analyses were integrated with differentially expressed genes from the TCGA repository to pinpoint T cell-associated genes. Utilizing Cox analysis, molecular subtypes and prognostic signatures were established and subsequently verified using the GEO dataset. The ensuing molecular and immunological distinctions, along with therapy sensitivity between the two sub-cohorts, were examined via the ESTIMATE, CIBERSORT, and ssGSEA methodologies. Compartmentalization, somatic mutation, nomogram development, chemotherapy sensitivity prediction, and potential drug prediction analyses were also conducted according to the risk signature. Additionally, real-time qPCR and the HPA database corroborated the mRNA and protein expression patterns of signature genes in LUAD tissues. In summary, this research yielded an innovative T cell marker gene-based signature with remarkable potential to prognosis and anticipate immunotherapeutic outcomes in LUAD patients.

A TCF4-dependent gene regulatory network confers resistance to immunotherapy in melanoma

To better understand intrinsic resistance to immune checkpoint blockade (ICB), we established a comprehensive view of the cellular architecture of the treatment-naive melanoma ecosystem and studied its evolution under ICB. Using single-cell, spatial multi-omics, we showed that the tumor microenvironment promotes the emergence of a complex melanoma transcriptomic landscape. Melanoma cells harboring a mesenchymal-like (MES) state, a population known to confer resistance to targeted therapy, were significantly enriched in early on-treatment biopsies from non-responders to ICB. TCF4 serves as the hub of this landscape by being a master regulator of the MES signature and a suppressor of the melanocytic and antigen presentation transcriptional programs. Targeting TCF4 genetically or pharmacologically, using a bromodomain inhibitor, increased immunogenicity and sensitivity of MES cells to ICB and targeted therapy. We thereby uncovered a TCF4-dependent regulatory network that orchestrates multiple transcriptional programs and contributes to resistance to both targeted therapy and ICB in melanoma.

Presence of onco-fetal neighborhoods in hepatocellular carcinoma is associated with relapse and response to immunotherapy

Onco-fetal reprogramming of the tumor ecosystem induces fetal developmental signatures in the tumor microenvironment, leading to immunosuppressive features. Here, we employed single-cell RNA sequencing, spatial transcriptomics and bulk RNA sequencing to delineate specific cell subsets involved in hepatocellular carcinoma (HCC) relapse and response to immunotherapy. We identified POSTN+ extracellular matrix cancer-associated fibroblasts (EM CAFs) as a prominent onco-fetal interacting hub, promoting tumor progression. Cell-cell communication and spatial transcriptomics analysis revealed crosstalk and co-localization of onco-fetal cells, including POSTN+ CAFs, FOLR2+ macrophages and PLVAP+ endothelial cells. Further analyses suggest an association between onco-fetal reprogramming and epithelial-mesenchymal transition (EMT), tumor cell proliferation and recruitment of Treg cells, ultimately influencing early relapse and response to immunotherapy. In summary, our study identifies POSTN+ CAFs as part of the HCC onco-fetal niche and highlights its potential influence in EMT, relapse and immunotherapy response, paving the way for the use of onco-fetal signatures for therapeutic stratification.

Single cell multi-omics reveal intra-cell-line heterogeneity across human cancer cell lines

Human cancer cell lines have long served as tools for cancer research and drug discovery, but the presence and the source of intra-cell-line heterogeneity remain elusive. Here, we perform single-cell RNA-sequencing and ATAC-sequencing on 42 and 39 human cell lines, respectively, to illustrate both transcriptomic and epigenetic heterogeneity within individual cell lines. Our data reveal that transcriptomic heterogeneity is frequently observed in cancer cell lines of different tissue origins, often driven by multiple common transcriptional programs. Copy number variation, as well as epigenetic variation and extrachromosomal DNA distribution all contribute to the detected intra-cell-line heterogeneity. Using hypoxia treatment as an example, we demonstrate that transcriptomic heterogeneity could be reshaped by environmental stress. Overall, our study performs single-cell multi-omics of commonly used human cancer cell lines and offers mechanistic insights into the intra-cell-line heterogeneity and its dynamics, which would serve as an important resource for future cancer cell line-based studies.

Multiomic analysis of cervical squamous cell carcinoma identifies cellular ecosystems with biological and clinical relevance

Cervical squamous cell carcinoma (CSCC) exhibits a limited response to immune-checkpoint blockade. Here we conducted a multiomic analysis encompassing single-cell RNA sequencing, spatial transcriptomics and spatial proteomics, combined with genetic and pharmacological perturbations to systematically develop a high-resolution and spatially resolved map of intratumoral expression heterogeneity in CSCC. Three tumor states (epithelial-cytokeratin, epithelial-immune (Epi-Imm) and epithelial senescence), recapitulating different stages of squamous differentiation, showed distinct tumor immune microenvironments. Bidirectional interactions between epithelial-cytokeratin malignant cells and immunosuppressive cancer-associated fibroblasts form an immune exclusionary microenvironment through transforming growth factor β pathway signaling mediated by FABP5. In Epi-Imm tumors, malignant cells interact with natural killer and T cells through interferon signaling. Preliminary analysis of samples from a cervical cancer clinical trial ( NCT04516616 ) demonstrated neoadjuvant chemotherapy induces a state transition to Epi-Imm, which correlates with pathological complete remission following treatment with immune-checkpoint blockade. These findings deepen the understanding of cellular state diversity in CSCC.

Predicting drug response from single-cell expression profiles of tumours

BACKGROUND

Intra-tumour heterogeneity (ITH) presents a significant obstacle in formulating effective treatment strategies in clinical practice. Single-cell RNA sequencing (scRNA-seq) has evolved as a powerful instrument for probing ITH at the transcriptional level, offering an unparalleled opportunity for therapeutic intervention.

RESULTS

Drug response prediction at the single-cell level is an emerging field of research that aims to improve the efficacy and precision of cancer treatments. Here, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a computational method that leverages publicly available pharmacogenomic screens from GDSC2, CTRP2, and PRISM and functional enrichment analysis to predict single-cell drug sensitivity from transcriptomic data. We validated DREEP extensively in vitro using several independent single-cell datasets with over 200 cancer cell lines and showed its accuracy and robustness. Additionally, we also applied DREEP to molecularly barcoded breast cancer cells and identified drugs that can selectively target specific cell populations.

CONCLUSIONS

DREEP provides an in silico framework to prioritize drugs from single-cell transcriptional profiles of tumours and thus helps in designing personalized treatment strategies and accelerating drug repurposing studies. DREEP is available at https://github.com/gambalab/DREEP .

Hierarchical and automated cell-type annotation and inference of cancer cell of origin with Census

MOTIVATION

Cell-type annotation is a time-consuming yet critical first step in the analysis of single-cell RNA-seq data, especially when multiple similar cell subtypes with overlapping marker genes are present. Existing automated annotation methods have a number of limitations, including requiring large reference datasets, high computation time, shallow annotation resolution, and difficulty in identifying cancer cells or their most likely cell of origin.

RESULTS

We developed Census, a biologically intuitive and fully automated cell-type identification method for single-cell RNA-seq data that can deeply annotate normal cells in mammalian tissues and identify malignant cells and their likely cell of origin. Motivated by the inherently stratified developmental programs of cellular differentiation, Census infers hierarchical cell-type relationships and uses gradient-boosted \decision trees that capitalize on nodal cell-type relationships to achieve high prediction speed and accuracy. When benchmarked on 44 atlas-scale normal and cancer, human and mouse tissues, Census significantly outperforms state-of-the-art methods across multiple metrics and naturally predicts the cell-of-origin of different cancers. Census is pretrained on the Tabula Sapiens to classify 175 cell-types from 24 organs; however, users can seamlessly train their own models for customized applications.

AVAILABILITY AND IMPLEMENTATION

Census is available at Zenodo https://zenodo.org/records/7017103 and on our Github https://github.com/sjdlabgroup/Census.

Tumor biology, immune infiltration and liver function define seven hepatocellular carcinoma subtypes linked to distinct drivers, survival and drug response

BACKGROUND & AIMS

Tumor heterogeneity is jointly determined by the components of the tumor ecosystem (TES) including tumor cells, immune cells, stromal cells, and non-cellular components. We aimed to identify subtypes using TES-related genes and determine subtype specific drivers and treatments for hepatocellular carcinoma (HCC).

METHODS

We collected 68 genesets depicting tumor biology, immune infiltration, and liver function, totaling 2831 genes, and collected mRNA profiles and clinical data for over 6000 tumors from 65 datasets in the GEO, TCGA, ICGC, and several other databases. We designed a three-step clustering pipeline to identify subtypes. The microenvironment, genomic alteration, and drug response differences were systematically compared among subtypes.

RESULTS

Seven subtypes (TES-1/2/3/4/5/6/7) were revealed in 159 tumors from the CHCC-HBV cohort. We constructed a single sample classifier using paired genes (sscpgsTES). TES subtypes were significantly associated with multiple clinical variables including etiology, and survival in 14 of 17 cohorts and the meta-cohort. TES-1 had the poorest prognosis and highest proliferation level. Both TES-2 and TES-7 were immune-enriched, however, TES-2 had a significantly worse prognosis, and hypoxic and immunosuppressive microenvironment. TES-4 had activated Wnt pathway, driven by CTNNB1 mutation. Good prognosis TES-6 exhibited the best differentiation. TES-5 and TES-3 were considered as novel subclasses by comparing with ten previous subtyping systems. TES-5 tumors had high AFP but good overall survival, and ∼45% of them harbored AXIN1 mutation. TES-3 was immune and stromal desert, may be driven by high copy number alteration burden, and had the poorest response to immune checkpoint inhibitor. TES-1 and TES-2 had significantly lower response to transarterial chemoembolization, but they showed significantly higher sensitivity to compound YM-155.

CONCLUSIONS

Tumor ecosystem subtypes expand existing HCC subtyping systems, have distinct drivers, prognosis, and treatment vulnerabilities.

Clustering malignant cell states using universally variable genes

Single-cell RNA sequencing (scRNA-seq) has revealed important insights into the heterogeneity of malignant cells. However, sample-specific genomic alterations often confound such analysis, resulting in patient-specific clusters that are difficult to interpret. Here, we present a novel approach to address the issue. By normalizing gene expression variances to identify universally variable genes (UVGs), we were able to reduce the formation of sample-specific clusters and identify underlying molecular hallmarks in malignant cells. In contrast to highly variable genes vulnerable to a specific sample bias, UVGs led to better detection of clusters corresponding to distinct malignant cell states. Our results demonstrate the utility of this approach for analyzing scRNA-seq data and suggest avenues for further exploration of malignant cell heterogeneity.

Integrated analysis of DNA methylome and transcriptome reveals SFRP1 and LIPG as potential drivers of ovarian cancer metastasis

OBJECTIVE

More than 75% of ovarian cancer patients are diagnosed at advanced stages and die of tumor cell metastasis. This study aimed to identify new epigenetic and transcriptomic alterations associated with ovarian cancer metastasis.

METHODS

Two cell sublines with low- and high-metastasis potentials were derived from the ovarian cancer cell line A2780. Genome-wide DNA methylome and transcriptome profiling were carried out in these two sublines by Reduced Representation Bisulfite Sequencing and RNA-seq technologies. Cell-based assays were conducted to support the clinical findings.

RESULTS

There are distinct DNA methylation and gene expression patterns between the two cell sublines with low- and high-metastasis potentials. Integrated analysis identified 33 methylation-induced genes potentially involved in ovarian cancer metastasis. The DNA methylation patterns of two of them (i.e., SFRP1 and LIPG) were further validated in human specimens, indicating that they were hypermethylated and downregulated in peritoneal metastatic ovarian carcinoma compared to primary ovarian carcinoma. Patients with lower SFRP1 and LIPG expression tend to have a worse prognosis. Functionally, knockdown of SFRP1 and LIPG promoted cell growth and migration, whereas their overexpression resulted in the opposite effects. In particular, knockdown of SFRP1 could phosphorylate GSK3β and increase β-catenin expression, leading to deregulated activation of the Wnt/β-catenin signaling.

CONCLUSION

Many systemic and important epigenetic and transcriptomic alterations occur in the progression of ovarian cancer. In particular, epigenetic silencing of SFRP1 and LIPG is a potential driver event in ovarian cancer metastasis. They can be used as prognostic biomarkers and therapeutic targets for ovarian cancer patients.

Identification and validation of a novel signature based on T cell marker genes to predict prognosis, immunotherapy response and chemotherapy sensitivity in head and neck squamous carcinoma by integrated analysis of single-cell and bulk RNA-sequencing

T cells are among the most potent anti-tumor cells that are found in humans. Our study sought to develop a reliable signature incorporating T cell marker genes (TMGs) for predicting the prognosis and therapy responsiveness of head and neck squamous cell carcinoma (HNSCC) patients. We downloaded scRNA-seq data from the GSE181919 to identify TMGs. Subsequently, we devised a 12 TMG signature in the TCGA HNSCC cohort by using LASSO analysis. Patients with high-risk scores were shown to experience unfavorable progression-free survival, disease-specific survival, and overall survival, which was validated in the GSE65858 cohort. Additionally, the nomogram integrated risk score and clinical features are more suitable for clinical application. The enrichment analyses of both pathways and functions showed that high- and low-risk patients had functionally related distinctions. Furthermore, analysis of the immunological landscape confirmed that the low-risk patients had a larger percentage of infiltrating immune cells as well as a higher incidence rate of immune-related events. In the meantime, a greater IPS score and expression of immune checkpoint genes suggested significantly favorable responsiveness to immunotherapy in low-risk patients. On the other hand, the high-risk patients had a greater degree of sensitivity to the chemotherapy agents, which included paclitaxel, gemcitabine, docetaxel, and cisplatin. Our finding revealed that this TMG signature independently functioned as a prognostic marker and guided individualized immunotherapy and chemotherapy selection for patients with HNSCC.

Understanding cervical cancer at single-cell resolution

Cervical cancer is now the fourth most prevalent malignancy in women worldwide, representing a tremendous burden of cancer. The heterogeneity of complex tumor ecosystem impacts tumorigenesis, malignant progression, and response to treatment; thus, a thorough understanding of the tumor ecosystem is vital for enhancing the prognosis of patients with cervical cancer. The rapid development and widespread use of single-cell sequencing have generated a new paradigm of cancer research, providing a comprehensive and in-depth understanding of cancers. In this review, we give an overview of the recent advances made by leveraging single-cell sequencing studies in the dissection of cervical cancer ecosystem heterogeneity. We highlight the evolution of the cervical cancer ecosystem during tumor initiation, progression, and treatment. High-resolution dissection of cervical cancer at the single-cell level has the potential to drive the development of targeted therapies and enable the realization of personalized medicine.

Comprehensive analyses for the coagulation and macrophage-related genes to reveal their joint roles in the prognosis and immunotherapy of lung adenocarcinoma patients

Purpose

This study aims to explore novel biomarkers related to the coagulation process and tumor-associated macrophage (TAM) infiltration in lung adenocarcinoma (LUAD).

Methods

The macrophage M2-related genes were obtained by Weighted Gene Co-expression Network Analysis (WGCNA) in bulk RNA-seq data, while the TAM marker genes were identified by analyzing the scRNA-seq data, and the coagulation-associated genes were obtained from MSigDB and KEGG databases. Survival analysis was performed for the intersectional genes. A risk score model was subsequently constructed based on the survival-related genes for prognosis prediction and validated in external datasets.

Results

In total, 33 coagulation and macrophage-related (COMAR) genes were obtained, 19 of which were selected for the risk score model construction. Finally, 10 survival-associated genes (APOE, ARRB2, C1QB, F13A1, FCGR2A, FYN, ITGB2, MMP9, OLR1, and VSIG4) were involved in the COMAR risk score model. According to the risk score, patients were equally divided into low- and high-risk groups, and the prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. The ROC curve indicated that the risk score model had high sensitivity and specificity, which was validated in multiple external datasets. Moreover, the model also had high efficacy in predicting the clinical outcomes of LUAD patients who received anti-PD-1/PD-L1 immunotherapy.

Conclusion

The COMAR risk score model constructed in this study has excellent predictive value for the prognosis and immunotherapeutic clinical outcomes of patients with LUAD, which provides potential biomarkers for the treatment and prognostic prediction.